Motor winding temperature estimation method based on thermal network model and related equipment

Abstract

The invention discloses a motor winding temperature estimation method based on a thermal network model and related equipment, and relates to the technical field of temperature detection, and the method comprises the steps: obtaining the target heating heat and the target water temperature of a motor; determining a motor winding temperature estimation value based on the target heating heat and the target water temperature through a thermal network model obtained through training; wherein the thermal network model is obtained by training in the following mode: obtaining training parameters corresponding to a plurality of time points respectively; the training parameters comprise water temperature, temperature sensor temperature and heating heat; calculating according to the water temperature, the temperature of the temperature sensor and the heating heat to obtain a thermal impedance change curve; and fitting the thermal impedance change curve to obtain a thermal network model.

Description

Technical Field

[0001] The present application relates to the field of temperature detection technology, and in particular to a method for estimating the temperature of a motor winding based on a thermal network model and related equipment. Background Art

[0002] At present, permanent magnet synchronous motors (PMSM) are widely used in aerospace, CNC machine tools, electric vehicles and other fields. They are important devices for converting electrical energy into mechanical energy, and ensuring their safe and efficient operation is of great significance. The temperature of the permanent magnet motor winding is an important factor affecting its operating reliability and life. When the motor winding temperature exceeds the limit temperature of the motor design material, it will weaken the insulation capacity of the winding and have an adverse effect on the operation of the motor. Online estimation of the motor stator winding temperature can monitor the safety status of the motor in real time, detect motor abnormalities as early as possible, and prevent the motor from being damaged due to overheating. Therefore, accurate online monitoring of the motor winding temperature is a very necessary technical means.

[0003] However, traditional winding temperature monitoring is to bury temperature sensors in the motor stator windings. Therefore, at the beginning of the design, temperature sensors need to be buried in multiple locations according to monitoring needs, which takes up space inside the motor, requires hardware costs, and has complex wiring. At the same time, if the temperature sensor is abnormal, it will cause the electric drive to overheat. Therefore, it is necessary to propose a motor winding temperature estimation method based on a thermal network model to at least solve some of the above problems. Summary of the invention

[0004] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0005] In a first aspect, an embodiment of the present application provides a method for estimating the temperature of a motor winding based on a thermal network model, the method comprising:

[0006] Obtain the target heat generation amount and target water flow temperature of the motor;

[0007] The thermal network model obtained through training is used to determine the estimated value of the motor winding temperature based on the target heat generation amount and the target water flow temperature;

[0008] The thermal network model is trained in the following way:

[0009] Obtaining training parameters corresponding to multiple time points respectively; the training parameters include water flow temperature, temperature sensor temperature and heat generation;

[0010] Calculating according to the water flow temperature, the temperature of the temperature sensor and the heat quantity to obtain a thermal impedance change curve;

[0011] The thermal impedance variation curve is fitted to obtain a thermal network model.

[0012] In one embodiment of the present invention, the step of obtaining the target heat amount of the motor includes:

[0013] Obtain bus terminal power, friction power and mechanical power;

[0014] The difference between the bus terminal power, the friction power and the mechanical power is integrated to obtain the heat.

[0015] In one embodiment of the present invention, the step of calculating according to the water flow temperature, the temperature sensor temperature and the heat amount to obtain the thermal impedance change curve comprises:

[0016] For each time point, the difference between the temperature of the temperature sensor and the water flow temperature corresponding to the time point is divided by the heat amount to obtain the thermal impedance value corresponding to the time point;

[0017] Based on the thermal impedance values ​​corresponding to each time point, a thermal impedance variation curve is obtained.

[0018] In one embodiment of the present invention, the model parameters of the thermal network model include thermal resistance and thermal capacitance;

[0019] The step of determining the estimated value of the motor winding temperature based on the target heat generation amount and the target water flow temperature by the thermal network model obtained through training comprises:

[0020] Multiplying the thermal resistance value by the thermal capacitance value to obtain a time constant, and obtaining a target thermal impedance value corresponding to the time constant;

[0021] The product of the target thermal impedance value and the power device loss value is summed with the water flow temperature to obtain an estimated value of the motor winding temperature.

[0022] In one embodiment of the present invention, the step of obtaining a target thermal impedance value corresponding to the time constant comprises:

[0023] The target thermal impedance value corresponding to the time constant is calculated using the following formula:

[0024]

[0025] Among them, τ i is the time constant, R i is the thermal resistance, e is a natural constant, and n is the order of the thermal network model.

[0026] In one embodiment of the present invention, the steps after the thermal network model obtained through training determines the estimated value of the motor winding temperature based on the target heat generation amount and the target water flow temperature include:

[0027] Determining a target operating parameter of the motor; the target operating parameter includes at least one of torque, flow rate and rotation speed;

[0028] Determine a target operating condition corresponding to the target operating parameter, and determine a target correction coefficient corresponding to the target operating condition based on a preset correlation relationship between the target operating condition and the correction coefficient;

[0029] The motor winding temperature estimation value is corrected based on the target correction coefficient to obtain a target motor winding temperature estimation value.

[0030] In one embodiment of the present invention, the correlation between the target operating condition and the correction coefficient is obtained in the following manner:

[0031] Acquire multiple target operating conditions; each target operating condition corresponds to multiple target operating parameters;

[0032] For each target operating condition, determining an estimated value of the motor winding temperature under target operating parameters corresponding to the target operating condition based on a thermal network model, and obtaining an actual temperature value through a temperature sensor;

[0033] Based on the difference between the actual temperature value and the estimated value of the motor winding temperature, a correction coefficient corresponding to the target operating condition is determined.

[0034] In a second aspect, the present application proposes a motor winding temperature estimation system based on a thermal network model, the system comprising: a data acquisition module and a calculation module;

[0035] The data acquisition module is configured to: acquire a target heating amount and a target water flow temperature of the motor;

[0036] The calculation module is configured to: determine the estimated value of the motor winding temperature based on the target heat quantity and the target water flow temperature through the trained thermal network model; wherein the thermal network model is trained in the following manner: obtaining training parameters corresponding to multiple time points respectively; the training parameters include the water flow temperature, the temperature sensor temperature and the heat quantity; calculating according to the water flow temperature, the temperature sensor temperature and the heat quantity to obtain a thermal impedance change curve; fitting the thermal impedance change curve to obtain a thermal network model.

[0037] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of a method for estimating the temperature of a motor winding based on a thermal network model as described in any one of the first aspects above when executing the computer program stored in the memory.

[0038] In a fourth aspect, the present application further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of a method for estimating the temperature of a motor winding based on a thermal network model according to any one of the first aspects are implemented.

[0039] In summary, the motor winding temperature estimation method based on the thermal network model in the embodiment of the present application trains the thermal network model through the target heat generation and target water flow temperature of the motor, and calculates based on the target heat generation and target water flow temperature to determine the estimated value of the motor winding temperature, thereby eliminating the motor temperature sensor, simplifying the internal space of the motor, streamlining the layout of the wiring harness, and saving hardware costs. It also avoids the problem of electric drive overheating caused by abnormal temperature sensors, and improves the stability of motor temperature inspection.

[0040] The motor winding temperature estimation method based on the thermal network model proposed in this application, and other advantages, objectives and features of this application will be reflected in part through the following description, and in part will be understood by technical personnel in this field through research and practice of this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0042] Figure 1 A schematic diagram of the process of estimating the temperature of a motor winding based on a thermal network model provided in an embodiment of the present application;

[0043] Figure 2 A schematic diagram of the structure of a motor winding temperature estimation system based on a thermal network model provided in an embodiment of the present application;

[0044] Figure 3 A schematic diagram of the structure of an electronic device for estimating the temperature of a motor winding based on a thermal network model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to better understand the technical solutions provided by the embodiments of this specification, the technical solutions of the embodiments of this specification are described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of this specification and the specific features in the embodiments are detailed descriptions of the technical solutions of the embodiments of this specification, rather than limitations on the technical solutions of this specification. In the absence of conflict, the embodiments of this specification and the technical features in the embodiments can be combined with each other.

[0046] In this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of more restrictions, the elements limited by the statement "comprise one..." do not exclude the existence of other identical elements in the process, method, article or equipment including the elements. The term "more than two" includes two or more than two situations.

[0047] See also Figure 1 , is a schematic diagram of a process flow of a method for estimating the temperature of a motor winding based on a thermal network model provided in an embodiment of the present application, which may specifically include:

[0048] S110, obtaining a target heating amount and a target water flow temperature of the motor;

[0049] For example, the laboratory provides a permanent magnet synchronous motor (or asynchronous motor) with a motor temperature sensor to obtain the target heat of the motor. The experimental towing bench sets the target water temperature to 45°C and the water flow rate to 9L / min. The torque of the machine under test is given as 0, and the accompanying machine drags the machine under test to rotate. The motor speed of the accompanying machine is set to 3000rpm, and the corresponding friction power P at this speed point is recorded. jloss0 Set the motor speed of the companion test machine to 3000rpm, the torque of the tested machine to 0, and record the temperature of the temperature sensor t0 and the bus terminal power P at this time. el0 , friction power P jloss0 , Mechanical power P mech0 ; Given a torque of 270 Nm, record the motor sensor temperature T at different times during the temperature reaching stability x , bus terminal power P elx , friction power P jlossx , Mechanical power P mechx .

[0050] S120, determining an estimated value of the motor winding temperature based on a target heat generation amount and a target water flow temperature by using the thermal network model obtained through training;

[0051] The thermal network model is trained in the following way:

[0052] Obtaining training parameters corresponding to multiple time points respectively; the training parameters include water flow temperature, temperature sensor temperature and heat generation;

[0053] Calculating according to the water flow temperature, the temperature of the temperature sensor and the heat quantity to obtain a thermal impedance change curve;

[0054] The thermal impedance variation curve is fitted to obtain a thermal network model.

[0055] For example, the thermal network model is a tool for predicting the motor winding temperature. It analyzes the thermal characteristics of the motor under different operating conditions and establishes the relationship between the input parameters (such as heat generation and water flow temperature) and the output results (the estimated value of the motor winding temperature). This estimated value of the motor winding temperature can provide an important reference for the operation control and thermal management of the motor to ensure that the motor operates within a safe temperature range.

[0056] In order to train the thermal network model, a large amount of training data needs to be collected, that is, the training parameters corresponding to multiple time points. Among them, the training parameters include water flow temperature, temperature sensor temperature and heat generation. The water flow temperature is the temperature of the water used to cool the motor, which will affect the heat dissipation effect of the motor. The temperature of the temperature sensor is the actual measured temperature inside the motor, which can reflect the thermal state of the motor. The heat generation indicates the amount of heat generated by the motor during operation.

[0057] Thermal impedance is an indicator of how easily heat is transferred inside the motor. By calculating the collected training parameters, the thermal impedance values ​​of the motor at different time points can be obtained. The changes in these thermal impedance values ​​over time are plotted as a curve, namely the thermal impedance change curve. This thermal impedance change curve reflects the changes in the thermal characteristics of the motor under different working conditions. By fitting the thermal impedance change curve, a thermal network model that can describe the thermal characteristics of the motor can be established. When fitting the thermal impedance change curve, it is necessary to fit the currently acquired thermal impedance values ​​using the least squares method, with the total variance target value minimized as the iteration target, and the thermal resistance values ​​R corresponding to each order of the 4th-order thermal network model can be calculated. i and heat capacity C i .

[0058] In summary, the motor winding temperature estimation method based on the thermal network model proposed in the embodiment of the present application trains the thermal network model through the target heat generation and target water flow temperature of the motor, and calculates based on the target heat generation and target water flow temperature to determine the estimated value of the motor winding temperature, thereby eliminating the motor temperature sensor, simplifying the internal space of the motor, streamlining the layout of the wiring harness, and saving hardware costs. It also avoids the problem of overheating of the electric drive if the temperature sensor is abnormal, and improves the stability of the motor temperature check.

[0059] In some examples, the step of obtaining a target heat amount of the motor includes:

[0060] Obtain bus terminal power, friction power and mechanical power;

[0061] The difference between the bus terminal power, the friction power and the mechanical power is integrated to obtain the heat.

[0062] For example, the bus terminal power P elx Refers to the power transmitted from the power source (such as a battery pack) to the motor. It can be calculated by measuring the bus current and bus voltage. Friction power P jlossx It is the power loss caused by friction between mechanical parts during the operation of the motor. Mechanical power P mechx It is the power output by the motor to drive the load. It can be calculated by measuring the speed and torque of the motor. The difference between the bus terminal power, friction power and mechanical power is integrated to obtain the heat generated, as shown in formula (1).

[0063] p(t)=∫(p el -p jloss -p mech )dt(1);

[0064] Among them, P el is the bus terminal power, P jloss is the friction power, P mech is the mechanical power.

[0065] In some examples, the step of calculating according to the water flow temperature, the temperature sensor temperature and the heat amount to obtain a thermal impedance change curve includes:

[0066] For each time point, the difference between the temperature of the temperature sensor and the water flow temperature corresponding to the time point is divided by the heat amount to obtain the thermal impedance value corresponding to the time point;

[0067] Based on the thermal impedance values ​​corresponding to each time point, a thermal impedance variation curve is obtained.

[0068] Exemplarily, the temperature of the temperature sensor reflects the actual temperature condition of the motor at that point in time, and the water flow temperature is the temperature of the water used to cool the motor. The difference between the two reflects the temperature difference of the motor relative to the cooling medium. Thermal impedance can be understood as the degree of resistance to heat transfer. The temperature difference is divided by the heat generated to obtain the thermal impedance value corresponding to the time point. Its physical meaning is that the thermal impedance value reflects the degree of resistance required to produce a specific temperature difference under a given heat amount. For example, if the thermal impedance value is large, it means that under the same heat amount, the temperature difference between the motor and the cooling medium will be large, that is, heat transfer is relatively difficult; conversely, if the thermal impedance value is small, it means that heat transfer is relatively easy.

[0069] As time goes by, the working state, heat generation, and cooling conditions of the motor may change, causing the thermal impedance value to change continuously. By collecting the thermal impedance values ​​at each time point and plotting them in chronological order, a thermal impedance change curve can be obtained. This thermal impedance change curve can intuitively show the trend of thermal impedance values ​​over time, reflecting the changes in the thermal characteristics of the motor at different working stages. For example, the thermal impedance value is large during the motor startup stage, and gradually decreases with the increase in running time and the effect of the cooling system; or when the load changes, the thermal impedance value will also change accordingly.

[0070] In some examples, the model parameters of the thermal network model include thermal resistance and thermal capacitance;

[0071] The step of determining the estimated value of the motor winding temperature based on the target heat generation amount and the target water flow temperature by the thermal network model obtained through training comprises:

[0072] Multiplying the thermal resistance value by the thermal capacitance value to obtain a time constant, and obtaining a target thermal impedance value corresponding to the time constant;

[0073] The product of the target thermal impedance value and the power device loss value is summed with the water flow temperature to obtain an estimated value of the motor winding temperature.

[0074] Exemplarily, the model parameters of the thermal network model include thermal resistance and thermal capacitance. The model parameters are discretized and converted into stator temperature estimation code parameters, and then the motor winding temperature under real-time working conditions is estimated. Among them, the thermal resistance value indicates the resistance encountered by heat during the transfer process. The larger the thermal resistance value, the slower the rate of heat transfer under the same temperature difference. For example, in a motor, the thermal resistance value can reflect the ease of heat transfer between different components inside the motor or between the motor and the surrounding environment. The thermal capacitance value indicates the ease of temperature change when an object absorbs or releases heat. The larger the thermal capacitance value, the smaller the temperature change of the object when absorbing or releasing the same amount of heat. In a motor, the thermal capacitance value can reflect the ability of various parts of the motor to store heat.

[0075] The time constant is an important parameter that reflects the dynamic characteristics of the system. In the thermal network model, the time constant is obtained by multiplying the thermal resistance value and the thermal capacitance value. It represents the time required for the temperature change of the system to reach a certain proportion of the final stable value when the system is subjected to heat input or output. The target thermal impedance value can be determined by the time constant. The product of the target thermal impedance value and the power device loss value is summed with the water flow temperature to obtain the estimated value of the motor winding temperature. The estimated value of the motor winding temperature is shown in formula (2):

[0076] T vj (t) = p(t) × Z thjc (t)+T case (t)(2);

[0077] Among them, T vj (t) is the estimated value of the motor winding temperature, p(t) is the heat generated, Z thjc (t) is the target thermal impedance value, T case (t) is the water flow temperature.

[0078] In some examples, the step of obtaining a target thermal impedance value corresponding to the time constant includes:

[0079] The target thermal impedance value corresponding to the time constant is calculated using the following formula:

[0080]

[0081] Among them, τ i is the time constant, R i is the thermal resistance, e is a natural constant, and n is the order of the thermal network model.

[0082] For example, the above thermal impedance change curve is expressed by formula (3), and the corresponding Foster thermal network model order needs to be designed. This application designs a 4th-order thermal network model. Based on this condition, the corresponding thermal resistance value R of each order needs to be obtained. i and heat capacity C i In formula (3), n is the order of the thermal network model. In this application, n=4.

[0083] In some examples, the steps after the thermal network model obtained through training determines the estimated value of the motor winding temperature based on the target heat generation amount and the target water flow temperature include:

[0084] Determining a target operating parameter of the motor; the target operating parameter includes at least one of torque, flow rate and rotation speed;

[0085] Determine a target operating condition corresponding to the target operating parameter, and determine a target correction coefficient corresponding to the target operating condition based on a preset correlation relationship between the target operating condition and the correction coefficient;

[0086] The motor winding temperature estimation value is corrected based on the target correction coefficient to obtain a target motor winding temperature estimation value.

[0087] Exemplarily, the estimated value of the motor winding temperature obtained above is obtained under fixed working conditions, and temperature correction under different conditions is also required. Determine the target operating parameters of the motor; the target operating parameters include at least one of torque, flow rate and speed. Determine the target operating conditions corresponding to the target operating parameters, and determine the target correction coefficient corresponding to the target operating conditions based on the association between the preset target operating conditions and the correction coefficient. When the target operating conditions of the current motor are determined, the corresponding correction coefficient can be found from this association. The function of the correction coefficient is to adjust the estimated value of the motor winding temperature so that it more accurately reflects the actual temperature under specific operating conditions. According to the water flow temperature of 45°C, the water flow rate of 9L / min, the motor speed of 3000rpm, the torque setting from 10Nm, the step size of 20Nm, and the loading to the maximum torque, at different torque points, record the motor sensor temperature and the estimated value of the motor winding temperature that change with time, and obtain the proportional relationship between the motor temperature sensor temperature and the estimated value of the motor winding temperature at this torque point. Finally, a one-dimensional table of the ratio of torque to actual temperature and estimated temperature is formed, as shown in Table 1:

[0088] Torque T / Nm 0 20 40 60 80 100 Correction factor k0 k1 k2 K3 K4 K5

[0089] Table 1

[0090] According to the water temperature of 45℃, water flow of 9L / min, torque setting from 100Nm, motor speed of 1000rpm, step size of 500rpm, and loading to the maximum speed of 18000rpm, record the temperature of the motor temperature sensor and the estimated value of the motor winding temperature over time at different speeds, and obtain the proportional relationship between the motor temperature sensor temperature and the estimated value of the motor winding temperature at this speed point. Finally, a one-dimensional table of the ratio of speed to actual temperature and estimated temperature is formed, as shown in Table 2:

[0091] Speed ​​n / rpm 0 1000 1500 2000 2500 3000 Correction factor a0 a1 a2 a3 a4 a5

[0092] Table 2

[0093] According to the water flow temperature of 45℃, the torque setting of 100Nm, the motor speed of 3000rpm, the water flow of 1L / min, the step size of 0.5L / min, and the maximum water flow of 10L / min, the temperature of the motor temperature sensor and the estimated value of the motor winding temperature are recorded over time at different water flow rates to obtain the proportional relationship between the motor temperature sensor temperature and the estimated value of the motor winding temperature at this speed point. Finally, a one-dimensional table of the ratio of speed to actual temperature and estimated temperature is formed, as shown in Table 3:

[0094] Water flow rate L / min 0 1 1.5 2 2.5 3 Correction factor b0 b1 b3 b4 b5 b6

[0095] Table 3

[0096] By correcting it with the target correction coefficient, the estimated value of the motor winding temperature can be made closer to the actual motor winding temperature. Finally, a thermal network estimation model under different torques, different flows, and different speeds will be formed, so that the estimated value of the motor winding temperature under all working conditions can be estimated. It is compared and verified with the temperature collected by the stator temperature sensor. When the verification is completed, the temperature sensor can be cancelled, and the above method can be used for the temperature estimation of the permanent magnet synchronous motor (or asynchronous motor) on the test bench or the whole vehicle, and for motor overtemperature protection.

[0097] In some examples, the association between the target operating condition and the correction coefficient is obtained in the following manner:

[0098] Acquire multiple target operating conditions; each target operating condition corresponds to multiple target operating parameters;

[0099] For each target operating condition, determining an estimated value of the motor winding temperature under target operating parameters corresponding to the target operating condition based on a thermal network model, and obtaining an actual temperature value through a temperature sensor;

[0100] Based on the difference between the actual temperature value and the estimated value of the motor winding temperature, a correction coefficient corresponding to the target operating condition is determined.

[0101] Exemplarily, multiple target operating conditions are obtained, each of which corresponds to multiple target operating parameters, indicating that various combinations of situations of the motor under different working conditions are obtained. These target operating conditions cover different combinations of parameters such as torque, flow, and speed, representing various actual working scenarios that the motor may face. For each specific target operating condition, a thermal network model is used to estimate the estimated value of the motor winding temperature under the condition. At the same time, during the actual operation of the motor, the actual temperature value of the motor under the target operating condition is directly measured by a temperature sensor installed on the motor. The estimated value of the motor winding temperature obtained by the thermal network model and the actual temperature value obtained by the temperature sensor are compared, and the difference between the two is calculated. This difference reflects the accuracy of the thermal network model under specific operating conditions. The correction coefficient corresponding to the target operating condition is determined based on this difference. The purpose of the correction coefficient is to adjust the output of the thermal network model so that the corrected estimated value of the motor winding temperature under the operating condition is closer to the actual temperature value.

[0102] like Figure 2 As shown, this application proposes a motor winding temperature estimation system based on a thermal network model, and the system includes: a data acquisition module 21 and a calculation module 22;

[0103] The data acquisition module 21 is configured to: acquire the target heat generation amount and target water flow temperature of the motor;

[0104] The calculation module 22 is configured to: determine the estimated value of the motor winding temperature based on the target heat quantity and the target water flow temperature through the trained thermal network model; wherein the thermal network model is trained in the following manner: obtaining training parameters corresponding to a plurality of time points; the training parameters include the water flow temperature, the temperature sensor temperature and the heat quantity; calculating according to the water flow temperature, the temperature sensor temperature and the heat quantity to obtain a thermal impedance change curve; fitting the thermal impedance change curve to obtain a thermal network model.

[0105] The effects of the above-mentioned system when applying the above-mentioned method can be found in the description of the above-mentioned method embodiment, which will not be repeated here.

[0106] like Figure 3 As shown, an embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any of the above-mentioned methods for estimating the motor winding temperature based on the thermal network model are implemented.

[0107] Since the electronic device introduced in this embodiment is a device used to implement a motor winding temperature estimation device based on a thermal network model in an embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0108] In the specific implementation process, when the computer program 311 is executed by the processor, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0109] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-readable program code.

[0111] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0114] An embodiment of the present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes the process of the LDPC decoding method of the solid-state hard disk controller.

[0115] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0117] In the several embodiments provided in the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0118] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0119] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0120] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0121] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

[0122] Although the preferred embodiments of this specification have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of this specification.

[0123] Obviously, those skilled in the art can make various changes and modifications to this specification without departing from the spirit and scope of this specification. Thus, if these modifications and variations of this specification fall within the scope of the claims of this specification and their equivalents, this specification is also intended to include these modifications and variations.

Claims

1. A method for estimating motor winding temperature based on a thermal network model, characterized in that: The method comprises: Obtain the target heat generation amount and target water flow temperature of the motor; The thermal network model obtained through training is used to determine the estimated value of the motor winding temperature based on the target heat generation amount and the target water flow temperature; The thermal network model is trained in the following way: Obtaining training parameters corresponding to multiple time points respectively; the training parameters include water flow temperature, temperature sensor temperature and heat generation; Calculating according to the water flow temperature, the temperature of the temperature sensor and the heat quantity to obtain a thermal impedance change curve; The thermal impedance variation curve is fitted to obtain a thermal network model.

2. The method for estimating motor winding temperature based on a thermal network model according to claim 1, characterized in that: The step of obtaining the target heat amount of the motor comprises: Obtain bus terminal power, friction power and mechanical power; The difference between the bus terminal power, the friction power and the mechanical power is integrated to obtain the heat.

3. The method for estimating motor winding temperature based on a thermal network model according to claim 1, characterized in that: The step of calculating according to the water flow temperature, the temperature of the temperature sensor and the heat amount to obtain the thermal impedance change curve comprises: For each time point, the difference between the temperature of the temperature sensor and the water flow temperature corresponding to the time point is divided by the heat amount to obtain the thermal impedance value corresponding to the time point; Based on the thermal impedance values ​​corresponding to each time point, a thermal impedance variation curve is obtained.

4. The method for estimating motor winding temperature based on a thermal network model according to claim 1, characterized in that: The model parameters of the thermal network model include thermal resistance value and thermal capacitance value; The step of determining the estimated value of the motor winding temperature based on the target heat generation amount and the target water flow temperature by the thermal network model obtained through training comprises: Multiplying the thermal resistance value by the thermal capacitance value to obtain a time constant, and obtaining a target thermal impedance value corresponding to the time constant; The product of the target thermal impedance value and the power device loss value is summed with the water flow temperature to obtain an estimated value of the motor winding temperature.

5. The method for estimating motor winding temperature based on thermal network model according to claim 4, characterized in that: The step of obtaining a target thermal impedance value corresponding to the time constant comprises: The target thermal impedance value corresponding to the time constant is calculated using the following formula: Among them, τ i is the time constant, R i is the thermal resistance, e is a natural constant, and n is the order of the thermal network model.

6. The method for estimating motor winding temperature based on thermal network model according to claim 1, characterized in that: The steps after the thermal network model obtained through training determines the estimated value of the motor winding temperature based on the target heat generation amount and the target water flow temperature include: Determining a target operating parameter of the motor; the target operating parameter includes at least one of torque, flow rate and rotation speed; Determine a target operating condition corresponding to the target operating parameter, and determine a target correction coefficient corresponding to the target operating condition based on a preset correlation relationship between the target operating condition and the correction coefficient; The motor winding temperature estimation value is corrected based on the target correction coefficient to obtain a target motor winding temperature estimation value.

7. The method for estimating motor winding temperature based on thermal network model according to claim 6, characterized in that: The correlation between the target operating condition and the correction coefficient is obtained in the following manner: Acquire multiple target operating conditions; each target operating condition corresponds to multiple target operating parameters; For each target operating condition, determining an estimated value of the motor winding temperature under target operating parameters corresponding to the target operating condition based on a thermal network model, and obtaining an actual temperature value through a temperature sensor; Based on the difference between the actual temperature value and the estimated value of the motor winding temperature, a correction coefficient corresponding to the target operating condition is determined.

8. A motor winding temperature estimation system based on a thermal network model, characterized in that: The system comprises: a data acquisition module and a calculation module; The data acquisition module is configured to: acquire a target heating amount and a target water flow temperature of the motor; The calculation module is configured to: determine the estimated value of the motor winding temperature based on the target heat quantity and the target water flow temperature through the trained thermal network model; wherein the thermal network model is trained in the following manner: obtaining training parameters corresponding to multiple time points respectively; the training parameters include the water flow temperature, the temperature sensor temperature and the heat quantity; calculating according to the water flow temperature, the temperature sensor temperature and the heat quantity to obtain a thermal impedance change curve; fitting the thermal impedance change curve to obtain a thermal network model.

9. An electronic device, comprising: A memory and a processor, characterized in that the processor is used to implement the steps of a motor winding temperature estimation method based on a thermal network model as described in any one of claims 1-7 when executing a computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for estimating the temperature of a motor winding based on a thermal network model as described in any one of claims 1 to 7 are implemented.

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